Introduction
Your firm just purchased an AI legal assistant. The vendor promises it understands Indian law, drafts better pleadings and researches faster than manual methods. But when you test it, the outputs feel generic. The case citations are mostly Supreme Court judgments. It doesn't understand your firm's drafting style and misses district court precedents you rely on. What went wrong? Nothing, actually. You're just seeing AI in its default state. The real question is: how will you customize this tool to actually work for your practice?
Understanding how legal AI is built and deployed isn't academic knowledge. It's practical information that determines whether your AI investment delivers value or becomes another unused piece of software gathering digital dust.
Fine-Tuning: Teaching AI Your Firm's Legal Language
You've hired a fresh law graduate. She's smart and knows basic legal principles. But she doesn't know how your firm drafts vakalatnamas, structures written statements or argues bail applications. So, you train her. You show her your firm's templates, explain your drafting preferences and review her work until she learns your approach.
Fine-Tuning is the process of taking a pre-trained AI model (like a general LLM) and further training it on a smaller, specialized dataset (e.g., your firm's documents) to adapt it to a specific task or style. [1] Fine-tuning works the same way with AI. A general legal AI knows broad legal concepts. But it doesn't know your specific practice. Fine-tuning means training the AI on your firm's documents so it learns your style, preferences and approach. But fine-tuning requires resources. You need enough quality documents, usually hundreds or thousands to train the AI effectively.
Prompt Engineering: Asking AI the Right Questions
You ask your junior associate to "research breach of contract." She returns three hundred cases with minimal context. Not helpful. You should have asked: "Find Karnataka High Court cases from the past five years on specific performance of sale agreements where the buyer delayed payment but the seller didn't issue a formal notice."
Prompt Engineering is the practice of carefully designing and structuring the text input (the prompt) given to an AI model to elicit the most accurate, relevant, and useful output. [2] It's how you communicate with AI to get useful outputs instead of generic responses. A bad prompt looks like this "Draft a notice under Section 138 Negotiable Instruments Act." While a good prompt may include: "Draft a legal notice under Section 138 NI Act for dishonor of a cheque dated 15th January 2025 for Rs. 2,50,000 issued by a proprietor of a trading firm in Mumbai. The cheque was dishonored on 28th January 2025 with the reason 'insufficient funds'. Include statutory demands and consequences under Section 138. Use formal but clear language."
See the difference? The second prompt gives the AI specific facts, jurisdiction, amount and tone preferences. Think of it as taking proper instructions from a client; you ask detailed questions to understand the full picture before advising. Do the same with AI.
Agentic AI: When AI Takes Multiple Steps Without Constant Supervision
You assign your associate a task: "Prepare for tomorrow's case hearing." You don't micromanage every step. You trust her to pull the case file, review relevant precedents, check for recent judgments and prepare a brief. She breaks down the task, completes each step and delivers the final work product.
Agentic AI (or AI Agents) refers to AI systems that can perceive their environment, make decisions, and execute a sequence of actions autonomously to achieve a specified goal, often breaking down complex tasks into steps. [3] But agentic AI requires more oversight, not less. When AI makes multiple sequential decisions, errors can compound.
Model Training vs Inference: Understanding What AI Does When
Your junior associate is either learning or working. During her training period, she studies cases and develops skills. Once trained, she applies those skills to actual client work. These are distinct phases requiring different resources. AI works the same way.
Model Training is the initial, compute-intensive phase where an AI algorithm learns patterns from a large dataset. [4] Inference is the phase where the trained model applies what it has learned to new input data to generate predictions or outputs. Inference is when the trained AI applies what it learned to your specific task. You ask it to draft a notice or research a question, and it generates an output based on its training.
Why does this matter? Because it affects cost, speed, and customization. If you want an AI tool customized to Indian law, someone must train it on Indian legal documents. This training phase is expensive.
On-Premise vs Cloud AI: Where Does Your AI Actually Run?
You're handling a sensitive corporate transaction. The client demands absolute confidentiality and you need AI assistance for document review, but you're worried about data security. Where will the client's documents actually be processed?
Cloud AI runs on the vendor's servers. You access it through your web browser. Your documents are uploaded to the vendor's infrastructure for processing. This is how most commercial legal AI tools work. While On-premise AI runs on your firm's own servers, inside your office network. All processing happens on your infrastructure.
Conclusion: What This Means for Your Practice
You don't need to build AI systems yourself. But you should understand how the AI tools you use are built and deployed. These aren't technical details to delegate to IT staff. These are practice management decisions that affect client service, data security, cost efficiency and professional responsibility. The lawyers who get the most value from AI aren't necessarily the most tech-savvy. They're the ones who understand what questions to ask and how to deploy AI.
Sources:
[1] Fine-Tuning (AI): Google Cloud, Fine-Tuning, https://cloud.google.com/use-cases/fine-tuning-ai-models?hl=en.
[2] Prompt Engineering: Google Cloud, Prompt Engineering, https://cloud.google.com/discover/what-is-prompt-engineering?hl=en.
[3] Agentic AI / AI Agents: Satyanand G., Agentic AI, Medium, https://medium.com/google-cloud/agentic-ai-beyond-generative-ai-a-deep-dive-7ece558f109f.
[4] Model Training & Inference: IBM, What is Machine Learning?, What is Machine Learning? | IBM.